The present invention relates to improved means and methods for evaluating, modeling, simulating, and predicting input-output (I/O) performance in a computer system.
The current direction of computer applications continues to approach a real-time environment in which the computing system is expected to respond quickly to end-user requests. The amount of delay from the moment a user makes a request to the moment the result is received (response delay) has become an important basis for evaluating computer performance. In today's environment of increasing processor speeds, the largest proportion of application wait time is due to I/O delays. Thus, the need for accurate and reliable means and methods for evaluating and predicting I/O delays has become more important than ever before.
The primary way of evaluating and predicting computer I/O performance has been to develop computer performance models. Such models have traditionally been developed using either probabilistic evaluation (analytic models) or discrete event simulation programs (simulation models).
An analytic model may be defined to be a model that accepts moment estimators (such as mean arrival and service times) as its input and, using a closed form or iterative method, produces moment estimators for the desired statistics (such as average wait time). Analytic modeling has focused on problems of queueing systems. Queueing network modeling has been evolving since the mid 1960s. It has been useful in providing solutions to many questions regarding computer performance. Although queueing systems are a subset of discrete systems, analytic modeling has been proven to be applicable in a wide range of computer performance evaluation problems and is the primary method used commercially today. The advantage of analytic modeling is in the low processor requirement. For models that are applicable to analytic modeling, runs can be performed in minutes, where simulation model requirements would be prohibitive.
However, there are some fundamental drawbacks to analytic modeling. A basic drawback is that not all discrete system can be evaluated. Furthermore, direct measurements have shown that many computer systems seriously violate the underlying assumptions of analytic models. I/O systems present a particular problem in this regard because of the large quantity and diverse nature of today's I/O workloads which create arrival and service distributions which are not only extremely variable, but also do not conform to those conventionally assumed for these models, thereby severely limiting the accuracy and reliability of the results obtained, while also limiting the ability to predict the performance of different I/O configurations. Also, the actual distributions of the analytic modeling parameters must often be simplified which further compromises accuracy. In addition, many systems that can be evaluated are intractable in that the calculations and memory requirements may grow non-linearly.
Simulation models are primarily useful to study computer performance at a high level of detail. A simulation model may be defined to be a model which accepts a set of measured or generated events (such as arrival or service requests) as its input and produces performance data corresponding thereto. Unfortunately, the level of detail is proportional to the processor requirements needed to sufficiently run the simulation. Thus, simulation is rarely used commercially because of the inordinate amount of processor time required to produce performance data. Furthermore, as is the case for analytic modeling, the ability of simulation models to predict the performance of different I/O configurations is severely limited because of the large quantity and diverse nature of modern-day I/O workloads.
Statistical techniques have been used in the prior art to augment and assist conventional analytic and simulation approaches, and also to aid in their evaluation. Statistical techniques have also been used to provide a submodel portion of an overall I/O simulation model. While such usage of statistical modeling offers the possibility of reducing the complexity and processor requirements of simulation models, it does not provide a solution to the predictability problem mentioned above.
In accordance with the present invention, improved means and methods are provided for computer I/O evaluation and prediction using a simplified simulation model which, to a significant extent, overcomes the drawbacks of presently known modeling approaches. This is achieved in a preferred embodiment of the invention by using I/O workload snapshots derived from actual measured customer I/O disk workloads for model input in conjunction which a simplified simulation model of a disk system which employs a combination of analytic, simulation and statistical modeling techniques to reduce processing time, and which operates to calculate I/O workload delays from the I/O snapshots on an individual basis, whereby in accordance with the invention, an unexpectedly fast simulation evaluate the performance of a particular disk system configuration as well as accurately predict the effect on performance of making changes in the configuration. The provision of such a model permits a user to effectively explore various solutions to disk performance problems and to choose a solution which will provide the needed performance.
The specific nature of the invention including its objects, features and advantages will become evident from the more detailed description provided herein taken in conjunction with the accompanying drawings.